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When Close Enough is Good Enough (sps25)

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Manage episode 514642714 series 2475293
Content provided by CCC media team. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by CCC media team or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://podcastplayer.com/legal.
Sometimes getting an approximate answer is super good enough. How do you check for duplicates, count unique users, or track item popularity when your dataset won’t fit in memory? Enter probabilistic data structures like Bloom filters, Count-Min Sketches, and HyperLogLog! This talk introduces these powerful tools, demonstrates simple implementations in Python, and gives you ideas on when to use them. Walk away ready to apply these techniques in your own projects - no advanced math required. about this event: https://talks.python-summit.ch/sps25/talk/QBXDUQ/
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2015 episodes

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Manage episode 514642714 series 2475293
Content provided by CCC media team. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by CCC media team or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://podcastplayer.com/legal.
Sometimes getting an approximate answer is super good enough. How do you check for duplicates, count unique users, or track item popularity when your dataset won’t fit in memory? Enter probabilistic data structures like Bloom filters, Count-Min Sketches, and HyperLogLog! This talk introduces these powerful tools, demonstrates simple implementations in Python, and gives you ideas on when to use them. Walk away ready to apply these techniques in your own projects - no advanced math required. about this event: https://talks.python-summit.ch/sps25/talk/QBXDUQ/
  continue reading

2015 episodes

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